脑认知科学的情感环路与蛋白质科学中不同蛋白质机器间相互作用形成关系网络的交叉与整合,构成了当今全球脑蛋白质组学研究的全新领域。抑郁障碍是一种严重危害公共健康的重大脑精神疾病,主要罹患青壮年。我国现有5 775万抑郁障碍患者,...脑认知科学的情感环路与蛋白质科学中不同蛋白质机器间相互作用形成关系网络的交叉与整合,构成了当今全球脑蛋白质组学研究的全新领域。抑郁障碍是一种严重危害公共健康的重大脑精神疾病,主要罹患青壮年。我国现有5 775万抑郁障碍患者,其中每年约有25万人死于自杀,抑郁障碍已成为全球首位致残性疾病。因此,围绕抑郁障碍全球脑科学研究热点,将系统构建抑郁障碍脑分子数据库及全景展现蛋白质、代谢分子的网络图谱,并基于所获得的大数据集从"Brain and body"抑郁症肠道微生物致病学说和抑郁障碍潜在蛋白质机器的两个角度研究和阐述难治性抑郁、抑郁伴自杀的病理生理过程,最终通过临床诊断试剂盒的研发、先导化合物的抗抑郁应用及全国多中心临床试验开展,以期构建抑郁障碍临床分子分型诊断体系,实现抑郁障碍个体化诊疗,开拓抑郁障碍治疗全新格局。提出的4个研究内容包括基于蛋白质机器的抑郁障碍脑分子网络图谱的构建、"微生物-肠-肝-脑"轴调控抑郁障碍分子网络的脑新型蛋白质机器研究、脑分子网络中自杀和难治性抑郁等关键蛋白质机器的功能机制研究以及抑郁障碍脑分子网络图谱的关键节点蛋白质复合体的筛查及临床分子分型研究。展开更多
The detection of single amino-acid variants （SAVs） usually depends on single-nucleotide polymorphisms （SNPs） database. Here, we describe a novel method that discovers SAVs at proteome level independent of SNPs dat...The detection of single amino-acid variants （SAVs） usually depends on single-nucleotide polymorphisms （SNPs） database. Here, we describe a novel method that discovers SAVs at proteome level independent of SNPs data. Using mass spectrometry-based de novo sequencing algorithm, peptide-candidates are identified and compared with theoretical protein database to generate SAVs under pairing strategy, which is followed by database re-searching to control false discovery rate. in human brain tissues, we can confidently identify known and novel protein variants with diverse origins. Combined with DNA/RNA sequencing, we verify SAVs derived from DNA mutations, RNA alternative splicing, and unknown post-transcriptional mechanisms. Furthermore, quantitative analysis in human brain tissues reveals several tissue-specific differential expressions of SAVs. This approach provides a novel access to high-throughput detection of protein variants, which may offer the potential for clinical biomarker discovery and mechanistic research.展开更多
文摘脑认知科学的情感环路与蛋白质科学中不同蛋白质机器间相互作用形成关系网络的交叉与整合,构成了当今全球脑蛋白质组学研究的全新领域。抑郁障碍是一种严重危害公共健康的重大脑精神疾病,主要罹患青壮年。我国现有5 775万抑郁障碍患者,其中每年约有25万人死于自杀,抑郁障碍已成为全球首位致残性疾病。因此,围绕抑郁障碍全球脑科学研究热点,将系统构建抑郁障碍脑分子数据库及全景展现蛋白质、代谢分子的网络图谱,并基于所获得的大数据集从"Brain and body"抑郁症肠道微生物致病学说和抑郁障碍潜在蛋白质机器的两个角度研究和阐述难治性抑郁、抑郁伴自杀的病理生理过程,最终通过临床诊断试剂盒的研发、先导化合物的抗抑郁应用及全国多中心临床试验开展,以期构建抑郁障碍临床分子分型诊断体系,实现抑郁障碍个体化诊疗,开拓抑郁障碍治疗全新格局。提出的4个研究内容包括基于蛋白质机器的抑郁障碍脑分子网络图谱的构建、"微生物-肠-肝-脑"轴调控抑郁障碍分子网络的脑新型蛋白质机器研究、脑分子网络中自杀和难治性抑郁等关键蛋白质机器的功能机制研究以及抑郁障碍脑分子网络图谱的关键节点蛋白质复合体的筛查及临床分子分型研究。
文摘The detection of single amino-acid variants （SAVs） usually depends on single-nucleotide polymorphisms （SNPs） database. Here, we describe a novel method that discovers SAVs at proteome level independent of SNPs data. Using mass spectrometry-based de novo sequencing algorithm, peptide-candidates are identified and compared with theoretical protein database to generate SAVs under pairing strategy, which is followed by database re-searching to control false discovery rate. in human brain tissues, we can confidently identify known and novel protein variants with diverse origins. Combined with DNA/RNA sequencing, we verify SAVs derived from DNA mutations, RNA alternative splicing, and unknown post-transcriptional mechanisms. Furthermore, quantitative analysis in human brain tissues reveals several tissue-specific differential expressions of SAVs. This approach provides a novel access to high-throughput detection of protein variants, which may offer the potential for clinical biomarker discovery and mechanistic research.
